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# https://github.com/lessw2020/Ranger-Deep-Learning-Optimizer
# This code was taken from the repo above and was not created by me (Fabian)! Full credit goes to the original authors
############

import math
import torch
from torch.optim.optimizer import Optimizer


class Ranger(Optimizer):

    def __init__(self, params, lr=1e-3, alpha=0.5, k=6, N_sma_threshhold=5, betas=(.95, 0.999), eps=1e-5,
                 weight_decay=0):
        # parameter checks
        if not 0.0 <= alpha <= 1.0:
            raise ValueError(f'Invalid slow update rate: {alpha}')
        if not 1 <= k:
            raise ValueError(f'Invalid lookahead steps: {k}')
        if not lr > 0:
            raise ValueError(f'Invalid Learning Rate: {lr}')
        if not eps > 0:
            raise ValueError(f'Invalid eps: {eps}')

        # parameter comments:
        # beta1 (momentum) of .95 seems to work better than .90...
        # N_sma_threshold of 5 seems better in testing than 4.
        # In both cases, worth testing on your dataset (.90 vs .95, 4 vs 5) to make sure which works best for you.

        # prep defaults and init torch.optim base
        defaults = dict(lr=lr, alpha=alpha, k=k, step_counter=0, betas=betas, N_sma_threshhold=N_sma_threshhold,
                        eps=eps, weight_decay=weight_decay)
        super().__init__(params, defaults)

        # adjustable threshold
        self.N_sma_threshhold = N_sma_threshhold

        # now we can get to work...
        # removed as we now use step from RAdam...no need for duplicate step counting
        # for group in self.param_groups:
        #    group["step_counter"] = 0
        # print("group step counter init")

        # look ahead params
        self.alpha = alpha
        self.k = k

        # radam buffer for state
        self.radam_buffer = [[None, None, None] for ind in range(10)]

        # self.first_run_check=0

        # lookahead weights
        # 9/2/19 - lookahead param tensors have been moved to state storage.
        # This should resolve issues with load/save where weights were left in GPU memory from first load, slowing down future runs.

        # self.slow_weights = [[p.clone().detach() for p in group['params']]
        #                     for group in self.param_groups]

        # don't use grad for lookahead weights
        # for w in it.chain(*self.slow_weights):
        #    w.requires_grad = False

    def __setstate__(self, state):
        print("set state called")
        super(Ranger, self).__setstate__(state)

    def step(self, closure=None):
        loss = None
        # note - below is commented out b/c I have other work that passes back the loss as a float, and thus not a callable closure.
        # Uncomment if you need to use the actual closure...

        # if closure is not None:
        # loss = closure()

        # Evaluate averages and grad, update param tensors
        for group in self.param_groups:

            for p in group['params']:
                if p.grad is None:
                    continue
                grad = p.grad.data.float()
                if grad.is_sparse:
                    raise RuntimeError('Ranger optimizer does not support sparse gradients')

                p_data_fp32 = p.data.float()

                state = self.state[p]  # get state dict for this param

                if len(state) == 0:  # if first time to run...init dictionary with our desired entries
                    # if self.first_run_check==0:
                    # self.first_run_check=1
                    # print("Initializing slow buffer...should not see this at load from saved model!")
                    state['step'] = 0
                    state['exp_avg'] = torch.zeros_like(p_data_fp32)
                    state['exp_avg_sq'] = torch.zeros_like(p_data_fp32)

                    # look ahead weight storage now in state dict
                    state['slow_buffer'] = torch.empty_like(p.data)
                    state['slow_buffer'].copy_(p.data)

                else:
                    state['exp_avg'] = state['exp_avg'].type_as(p_data_fp32)
                    state['exp_avg_sq'] = state['exp_avg_sq'].type_as(p_data_fp32)

                # begin computations
                exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
                beta1, beta2 = group['betas']

                # compute variance mov avg
                exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
                # compute mean moving avg
                exp_avg.mul_(beta1).add_(1 - beta1, grad)

                state['step'] += 1

                buffered = self.radam_buffer[int(state['step'] % 10)]
                if state['step'] == buffered[0]:
                    N_sma, step_size = buffered[1], buffered[2]
                else:
                    buffered[0] = state['step']
                    beta2_t = beta2 ** state['step']
                    N_sma_max = 2 / (1 - beta2) - 1
                    N_sma = N_sma_max - 2 * state['step'] * beta2_t / (1 - beta2_t)
                    buffered[1] = N_sma
                    if N_sma > self.N_sma_threshhold:
                        step_size = math.sqrt(
                            (1 - beta2_t) * (N_sma - 4) / (N_sma_max - 4) * (N_sma - 2) / N_sma * N_sma_max / (
                                        N_sma_max - 2)) / (1 - beta1 ** state['step'])
                    else:
                        step_size = 1.0 / (1 - beta1 ** state['step'])
                    buffered[2] = step_size

                if group['weight_decay'] != 0:
                    p_data_fp32.add_(-group['weight_decay'] * group['lr'], p_data_fp32)

                if N_sma > self.N_sma_threshhold:
                    denom = exp_avg_sq.sqrt().add_(group['eps'])
                    p_data_fp32.addcdiv_(-step_size * group['lr'], exp_avg, denom)
                else:
                    p_data_fp32.add_(-step_size * group['lr'], exp_avg)

                p.data.copy_(p_data_fp32)

                # integrated look ahead...
                # we do it at the param level instead of group level
                if state['step'] % group['k'] == 0:
                    slow_p = state['slow_buffer']  # get access to slow param tensor
                    slow_p.add_(self.alpha, p.data - slow_p)  # (fast weights - slow weights) * alpha
                    p.data.copy_(slow_p)  # copy interpolated weights to RAdam param tensor

        return loss